Greenhouse Plant Smart Monitoring System
AI-Powered Early Disease Detection and Plant Monitoring for Sustainable Agriculture
problem
The client, a startup leveraging AI to monitor plants for early-stage disease detection, faced significant challenges in optimizing their technological approach to meet business needs. With modern agriculture increasingly adopting high-tech solutions, there remains a gap in cost-effective, end-to-end pipeline development for specific tasks. Farm income, particularly for high-profit plants like medical cannabis, hinges on effective plant growth monitoring and disease prevention. The client sought to automate the manual labor involved in observing hemp plants to maximize yields. To achieve this, they partnered with Interactivated to implement advanced AI and data analytics technologies.
problem
The client, a startup leveraging AI to monitor plants for early-stage disease detection, faced significant challenges in optimizing their technological approach to meet business needs. With modern agriculture increasingly adopting high-tech solutions, there remains a gap in cost-effective, end-to-end pipeline development for specific tasks. Farm income, particularly for high-profit plants like medical cannabis, hinges on effective plant growth monitoring and disease prevention. The client sought to automate the manual labor involved in observing hemp plants to maximize yields. To achieve this, they partnered with Interactivated to implement advanced AI and data analytics technologies.
solution
Our solution involved developing a sophisticated multi-staged pipeline for plant monitoring and disease detection. The first stage focused on hemp flower detection with 80% accuracy, achieved by segmenting images based on cannabis growth stages and training custom models for optimal performance. We then developed a disease classification model with 90% accuracy, capable of identifying 11 different types of leaves and conducting in-depth health analysis. This model identified specific diseases by detecting various symptoms and their interrelations. The pipeline included image segmentation to detect leaves and trunks for health checking, followed by multi-labeled classification of symptoms. We employed a custom Pyramid CNN deep learning model for leaf segmentation, utilizing transfer learning and various post-processing methods to improve accuracy. The leaf filtering model further refined the data by extracting and preprocessing clear leaf images. Our final solution involved training a multi-output model using EfficientNet, optimizing performance with custom thresholds and a new loss function, resulting in significant accuracy improvements.
solution
Our solution involved developing a sophisticated multi-staged pipeline for plant monitoring and disease detection. The first stage focused on hemp flower detection with 80% accuracy, achieved by segmenting images based on cannabis growth stages and training custom models for optimal performance. We then developed a disease classification model with 90% accuracy, capable of identifying 11 different types of leaves and conducting in-depth health analysis. This model identified specific diseases by detecting various symptoms and their interrelations. The pipeline included image segmentation to detect leaves and trunks for health checking, followed by multi-labeled classification of symptoms. We employed a custom Pyramid CNN deep learning model for leaf segmentation, utilizing transfer learning and various post-processing methods to improve accuracy. The leaf filtering model further refined the data by extracting and preprocessing clear leaf images. Our final solution involved training a multi-output model using EfficientNet, optimizing performance with custom thresholds and a new loss function, resulting in significant accuracy improvements.
results
Our collaboration resulted in a robust pipeline that effectively addresses the client s needs for plant growth monitoring and early-stage disease detection. The model achieved an F1-score of up to 90% across 11 classes, enabling timely treatment or withdrawal of affected plants and saving nearly 25% of the crop. This solution not only improved the accuracy of disease detection but also optimized infrastructure costs by reducing data usage without compromising analytic precision. The comprehensive pipeline stages - image segmentation, leaf processing, and disease classification - demonstrated the potential of computer vision and machine learning technologies to drive sustainable agricultural practices. Our approach allowed the client to meet their business goals, providing a scalable and efficient method for monitoring plant health and enhancing crop yields.
results
Our collaboration resulted in a robust pipeline that effectively addresses the client s needs for plant growth monitoring and early-stage disease detection. The model achieved an F1-score of up to 90% across 11 classes, enabling timely treatment or withdrawal of affected plants and saving nearly 25% of the crop. This solution not only improved the accuracy of disease detection but also optimized infrastructure costs by reducing data usage without compromising analytic precision. The comprehensive pipeline stages - image segmentation, leaf processing, and disease classification - demonstrated the potential of computer vision and machine learning technologies to drive sustainable agricultural practices. Our approach allowed the client to meet their business goals, providing a scalable and efficient method for monitoring plant health and enhancing crop yields.
tech stack
tech stack
